Overview

Brought to you by YData

Dataset statistics

Number of variables9
Number of observations30000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.3 MiB
Average record size in memory80.0 B

Variable types

Numeric8
DateTime1

Alerts

Temperature is highly overall correlated with idHigh correlation
feature_AA is highly overall correlated with feature_BAHigh correlation
feature_AB is highly overall correlated with feature_BBHigh correlation
feature_BA is highly overall correlated with feature_AAHigh correlation
feature_BB is highly overall correlated with feature_ABHigh correlation
id is highly overall correlated with TemperatureHigh correlation
id is uniformly distributed Uniform
id has unique values Unique
date has unique values Unique
feature_AB has 746 (2.5%) zeros Zeros
feature_BB has 458 (1.5%) zeros Zeros
feature_CB has 388 (1.3%) zeros Zeros

Reproduction

Analysis started2025-05-08 19:37:46.437336
Analysis finished2025-05-08 19:37:53.853668
Duration7.42 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

id
Real number (ℝ)

High correlation  Uniform  Unique 

Distinct30000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32257.588
Minimum0
Maximum64319
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size468.8 KiB
2025-05-08T21:37:53.936755image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3283.9
Q116178
median32344.5
Q348346.5
95-th percentile61079.1
Maximum64319
Range64319
Interquartile range (IQR)32168.5

Descriptive statistics

Standard deviation18567.49
Coefficient of variation (CV)0.5756007
Kurtosis-1.2034111
Mean32257.588
Median Absolute Deviation (MAD)16075
Skewness-0.0080484406
Sum9.6772765 × 108
Variance3.447517 × 108
MonotonicityNot monotonic
2025-05-08T21:37:54.060684image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45058 1
 
< 0.1%
13071 1
 
< 0.1%
19675 1
 
< 0.1%
15329 1
 
< 0.1%
5348 1
 
< 0.1%
47441 1
 
< 0.1%
37199 1
 
< 0.1%
16775 1
 
< 0.1%
61653 1
 
< 0.1%
25937 1
 
< 0.1%
Other values (29990) 29990
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
2 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
9 1
< 0.1%
12 1
< 0.1%
15 1
< 0.1%
16 1
< 0.1%
18 1
< 0.1%
21 1
< 0.1%
ValueCountFrequency (%)
64319 1
< 0.1%
64316 1
< 0.1%
64313 1
< 0.1%
64312 1
< 0.1%
64311 1
< 0.1%
64309 1
< 0.1%
64306 1
< 0.1%
64304 1
< 0.1%
64301 1
< 0.1%
64298 1
< 0.1%

date
Date

Unique 

Distinct30000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size468.8 KiB
Minimum2016-07-01 00:00:00
Maximum2018-05-01 23:45:00
Invalid dates0
Invalid dates (%)0.0%
2025-05-08T21:37:54.190609image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-08T21:37:54.322533image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

feature_AA
Real number (ℝ)

High correlation 

Distinct629
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.5379205
Minimum-20.094
Maximum24.18
Zeros167
Zeros (%)0.6%
Negative3756
Negative (%)12.5%
Memory size468.8 KiB
2025-05-08T21:37:54.461439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-20.094
5-th percentile-8.1719999
Q15.961
median8.908
Q311.721
95-th percentile15.673
Maximum24.18
Range44.274
Interquartile range (IQR)5.7599998

Descriptive statistics

Standard deviation6.8754307
Coefficient of variation (CV)0.9121124
Kurtosis2.1032875
Mean7.5379205
Median Absolute Deviation (MAD)2.8800001
Skewness-1.3868284
Sum226137.61
Variance47.271548
MonotonicityNot monotonic
2025-05-08T21:37:54.579860image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.11900043 251
 
0.8%
10.71700001 239
 
0.8%
6.96600008 220
 
0.7%
10.24800014 217
 
0.7%
8.43900013 216
 
0.7%
6.899000168 212
 
0.7%
9.578000069 212
 
0.7%
7.636000156 211
 
0.7%
7.368000031 207
 
0.7%
6.497000217 206
 
0.7%
Other values (619) 27809
92.7%
ValueCountFrequency (%)
-20.09399986 1
< 0.1%
-20.02700043 1
< 0.1%
-19.75900078 1
< 0.1%
-19.69199944 1
< 0.1%
-19.625 1
< 0.1%
-19.35700035 1
< 0.1%
-19.29000092 1
< 0.1%
-19.22299957 1
< 0.1%
-19.0890007 2
< 0.1%
-19.02199936 1
< 0.1%
ValueCountFrequency (%)
24.18000031 1
< 0.1%
23.44300079 2
< 0.1%
23.17499924 1
< 0.1%
23.04100037 1
< 0.1%
22.70599937 1
< 0.1%
22.63899994 2
< 0.1%
22.5720005 1
< 0.1%
22.43799973 2
< 0.1%
22.30400085 2
< 0.1%
22.23699951 1
< 0.1%

feature_AB
Real number (ℝ)

High correlation  Zeros 

Distinct211
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1376469
Minimum-4.823
Maximum10.047
Zeros746
Zeros (%)2.5%
Negative4203
Negative (%)14.0%
Memory size468.8 KiB
2025-05-08T21:37:54.696792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-4.823
5-th percentile-1.072
Q10.67000002
median2.076
Q33.55
95-th percentile5.559
Maximum10.047
Range14.87
Interquartile range (IQR)2.8799999

Descriptive statistics

Standard deviation2.0120199
Coefficient of variation (CV)0.94123121
Kurtosis-0.21896622
Mean2.1376469
Median Absolute Deviation (MAD)1.4070001
Skewness0.176459
Sum64129.406
Variance4.0482242
MonotonicityNot monotonic
2025-05-08T21:37:54.821706image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 746
 
2.5%
1.674000025 493
 
1.6%
1.807999969 476
 
1.6%
1.60800004 455
 
1.5%
2.075999975 442
 
1.5%
3.951999903 420
 
1.4%
1.406999946 418
 
1.4%
2.477999926 416
 
1.4%
2.142999887 415
 
1.4%
2.344000101 400
 
1.3%
Other values (201) 25319
84.4%
ValueCountFrequency (%)
-4.822999954 1
 
< 0.1%
-4.756000042 1
 
< 0.1%
-4.622000217 1
 
< 0.1%
-4.554999828 1
 
< 0.1%
-4.487999916 1
 
< 0.1%
-4.287000179 1
 
< 0.1%
-4.21999979 1
 
< 0.1%
-4.085999966 2
< 0.1%
-3.951999903 3
< 0.1%
-3.88499999 2
< 0.1%
ValueCountFrequency (%)
10.04699993 1
 
< 0.1%
9.979999542 1
 
< 0.1%
9.913000107 2
< 0.1%
9.711999893 1
 
< 0.1%
9.51099968 2
< 0.1%
9.444000244 1
 
< 0.1%
9.243000031 2
< 0.1%
9.175999641 4
< 0.1%
9.109000206 1
 
< 0.1%
9.041999817 2
< 0.1%

feature_BA
Real number (ℝ)

High correlation 

Distinct1051
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4952391
Minimum-22.316
Maximum17.767
Zeros166
Zeros (%)0.6%
Negative4762
Negative (%)15.9%
Memory size468.8 KiB
2025-05-08T21:37:54.949647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-22.316
5-th percentile-11.265
Q13.553
median6.0409999
Q38.6350002
95-th percentile11.584
Maximum17.767
Range40.083
Interquartile range (IQR)5.0820003

Descriptive statistics

Standard deviation6.62799
Coefficient of variation (CV)1.4744466
Kurtosis2.5535231
Mean4.4952391
Median Absolute Deviation (MAD)2.5580001
Skewness-1.6320081
Sum134857.17
Variance43.930251
MonotonicityNot monotonic
2025-05-08T21:37:55.079559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.106999874 180
 
0.6%
0 166
 
0.6%
4.406000137 155
 
0.5%
8.670999527 154
 
0.5%
6.324999809 151
 
0.5%
6.894000053 151
 
0.5%
4.441999912 148
 
0.5%
4.335000038 142
 
0.5%
5.401000023 139
 
0.5%
4.547999859 138
 
0.5%
Other values (1041) 28476
94.9%
ValueCountFrequency (%)
-22.31599998 1
 
< 0.1%
-22.17399979 1
 
< 0.1%
-22.03199959 1
 
< 0.1%
-21.96100044 1
 
< 0.1%
-21.74699974 2
< 0.1%
-21.56999969 2
< 0.1%
-21.5340004 1
 
< 0.1%
-21.4279995 1
 
< 0.1%
-21.28499985 4
< 0.1%
-21.25 2
< 0.1%
ValueCountFrequency (%)
17.7670002 1
 
< 0.1%
17.5189991 2
 
< 0.1%
17.37700081 1
 
< 0.1%
17.3409996 1
 
< 0.1%
17.09199905 1
 
< 0.1%
16.98600006 1
 
< 0.1%
16.77300072 1
 
< 0.1%
16.59499931 2
 
< 0.1%
16.41699982 2
 
< 0.1%
16.34600067 44
0.1%

feature_BB
Real number (ℝ)

High correlation  Zeros 

Distinct331
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8183665
Minimum-5.9699998
Maximum7.8530002
Zeros458
Zeros (%)1.5%
Negative9064
Negative (%)30.2%
Memory size468.8 KiB
2025-05-08T21:37:55.224475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-5.9699998
5-th percentile-2.2390001
Q1-0.391
median0.85299999
Q32.132
95-th percentile3.6960001
Maximum7.8530002
Range13.823
Interquartile range (IQR)2.523

Descriptive statistics

Standard deviation1.8100692
Coefficient of variation (CV)2.2118075
Kurtosis-0.26917973
Mean0.8183665
Median Absolute Deviation (MAD)1.279
Skewness-0.061407925
Sum24550.995
Variance3.2763504
MonotonicityNot monotonic
2025-05-08T21:37:55.357403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 458
 
1.5%
0.7820000052 303
 
1.0%
2.239000082 297
 
1.0%
0.5329999924 293
 
1.0%
0.8880000114 290
 
1.0%
0.9589999914 280
 
0.9%
0.425999999 277
 
0.9%
0.8169999719 272
 
0.9%
0.6039999723 271
 
0.9%
0.8529999852 262
 
0.9%
Other values (321) 26997
90.0%
ValueCountFrequency (%)
-5.96999979 1
 
< 0.1%
-5.934000015 2
< 0.1%
-5.899000168 1
 
< 0.1%
-5.756999969 2
< 0.1%
-5.366000175 1
 
< 0.1%
-5.295000076 3
< 0.1%
-5.117000103 2
< 0.1%
-5.046000004 1
 
< 0.1%
-4.93900013 1
 
< 0.1%
-4.903999805 1
 
< 0.1%
ValueCountFrequency (%)
7.853000164 1
< 0.1%
7.782000065 1
< 0.1%
7.604000092 1
< 0.1%
7.355999947 1
< 0.1%
7.320000172 1
< 0.1%
7.284999847 1
< 0.1%
7.177999973 1
< 0.1%
7.143000126 1
< 0.1%
7.071000099 1
< 0.1%
7 1
< 0.1%

feature_CA
Real number (ℝ)

Distinct253
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0407972
Minimum-1.188
Maximum8.4370003
Zeros111
Zeros (%)0.4%
Negative2
Negative (%)< 0.1%
Memory size468.8 KiB
2025-05-08T21:37:55.484182image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1.188
5-th percentile1.614
Q12.2839999
median2.8329999
Q33.5940001
95-th percentile5.1494998
Maximum8.4370003
Range9.6250002
Interquartile range (IQR)1.3100002

Descriptive statistics

Standard deviation1.1681532
Coefficient of variation (CV)0.38416018
Kurtosis3.2299462
Mean3.0407972
Median Absolute Deviation (MAD)0.60899997
Skewness1.3178629
Sum91223.917
Variance1.3645819
MonotonicityNot monotonic
2025-05-08T21:37:55.613097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.345000029 476
 
1.6%
2.559000015 470
 
1.6%
2.680000067 463
 
1.5%
2.315000057 462
 
1.5%
2.588999987 461
 
1.5%
2.375999928 459
 
1.5%
2.650000095 455
 
1.5%
2.437000036 447
 
1.5%
2.467000008 447
 
1.5%
2.710999966 444
 
1.5%
Other values (243) 25416
84.7%
ValueCountFrequency (%)
-1.187999964 1
 
< 0.1%
-0.7009999752 1
 
< 0.1%
0 111
0.4%
0.1220000014 1
 
< 0.1%
0.4869999886 1
 
< 0.1%
0.5180000067 1
 
< 0.1%
0.5479999781 1
 
< 0.1%
0.6090000272 4
 
< 0.1%
0.6399999857 3
 
< 0.1%
0.6700000167 4
 
< 0.1%
ValueCountFrequency (%)
8.437000275 2
 
< 0.1%
8.406999588 1
 
< 0.1%
8.345999718 1
 
< 0.1%
8.31499958 1
 
< 0.1%
8.284999847 257
0.9%
8.25399971 1
 
< 0.1%
8.163000107 1
 
< 0.1%
8.133000374 1
 
< 0.1%
8.102000237 1
 
< 0.1%
8.07199955 1
 
< 0.1%

feature_CB
Real number (ℝ)

Zeros 

Distinct126
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.81822953
Minimum-1.3710001
Maximum2.4979999
Zeros388
Zeros (%)1.3%
Negative3102
Negative (%)10.3%
Memory size468.8 KiB
2025-05-08T21:37:55.738025image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1.3710001
5-th percentile-0.63999999
Q10.63999999
median0.94400001
Q31.1569999
95-th percentile1.523
Maximum2.4979999
Range3.869
Interquartile range (IQR)0.51699996

Descriptive statistics

Standard deviation0.60156235
Coefficient of variation (CV)0.7352
Kurtosis1.8780241
Mean0.81822953
Median Absolute Deviation (MAD)0.27399999
Skewness-1.3540143
Sum24546.886
Variance0.36187726
MonotonicityNot monotonic
2025-05-08T21:37:56.063838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.097000003 1104
 
3.7%
1.065999985 1060
 
3.5%
0.9750000238 1057
 
3.5%
1.004999995 1030
 
3.4%
1.036000013 996
 
3.3%
0.9440000057 956
 
3.2%
0.8529999852 953
 
3.2%
1.126999974 946
 
3.2%
0.8220000267 922
 
3.1%
1.156999946 883
 
2.9%
Other values (116) 20093
67.0%
ValueCountFrequency (%)
-1.371000051 2
 
< 0.1%
-1.340000033 2
 
< 0.1%
-1.309999943 1
 
< 0.1%
-1.248999953 4
 
< 0.1%
-1.218000054 6
 
< 0.1%
-1.187999964 13
 
< 0.1%
-1.156999946 20
 
0.1%
-1.126999974 42
0.1%
-1.097000003 71
0.2%
-1.065999985 74
0.2%
ValueCountFrequency (%)
2.497999907 2
 
< 0.1%
2.467000008 1
 
< 0.1%
2.437000036 1
 
< 0.1%
2.375999928 3
 
< 0.1%
2.345000029 2
 
< 0.1%
2.315000057 1
 
< 0.1%
2.28399992 5
< 0.1%
2.253999949 4
< 0.1%
2.223999977 5
< 0.1%
2.193000078 8
< 0.1%

Temperature
Real number (ℝ)

High correlation 

Distinct686
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.590693
Minimum-4.1500001
Maximum46.007
Zeros213
Zeros (%)0.7%
Negative276
Negative (%)0.9%
Memory size468.8 KiB
2025-05-08T21:37:56.207754image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-4.1500001
5-th percentile2.5320001
Q16.7529998
median11.889
Q318.712
95-th percentile32.077999
Maximum46.007
Range50.157
Interquartile range (IQR)11.959

Descriptive statistics

Standard deviation8.8143823
Coefficient of variation (CV)0.6485602
Kurtosis0.5037765
Mean13.590693
Median Absolute Deviation (MAD)5.8389997
Skewness0.87254582
Sum407720.78
Variance77.693336
MonotonicityNot monotonic
2025-05-08T21:37:56.344678image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 213
 
0.7%
6.752999783 169
 
0.6%
5.697999954 158
 
0.5%
4.994999886 153
 
0.5%
10.13000011 153
 
0.5%
10.27099991 152
 
0.5%
9.918999672 152
 
0.5%
5.90899992 150
 
0.5%
10.06000042 149
 
0.5%
5.205999851 147
 
0.5%
Other values (676) 28404
94.7%
ValueCountFrequency (%)
-4.150000095 1
 
< 0.1%
-3.938999891 2
< 0.1%
-3.868999958 1
 
< 0.1%
-3.799000025 1
 
< 0.1%
-3.657999992 1
 
< 0.1%
-3.588000059 3
< 0.1%
-3.51699996 1
 
< 0.1%
-3.447000027 2
< 0.1%
-3.377000093 2
< 0.1%
-3.305999994 4
< 0.1%
ValueCountFrequency (%)
46.00699997 1
< 0.1%
45.51499939 1
< 0.1%
45.44400024 2
< 0.1%
45.3030014 1
< 0.1%
45.23300171 1
< 0.1%
45.1629982 1
< 0.1%
45.09199905 2
< 0.1%
45.02199936 1
< 0.1%
44.95199966 2
< 0.1%
44.88100052 1
< 0.1%

Interactions

2025-05-08T21:37:52.689512image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-08T21:37:47.035383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-08T21:37:47.882908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-08T21:37:48.735402image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-08T21:37:49.532328image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-08T21:37:50.340428image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-08T21:37:51.116009image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-08T21:37:51.858794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-08T21:37:52.782481image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-08T21:37:47.145319image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-08T21:37:47.978853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-08T21:37:48.839342image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-08T21:37:49.635275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-08T21:37:50.439276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-08T21:37:51.214946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-08T21:37:51.963754image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-05-08T21:37:52.589582image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

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Temperaturefeature_AAfeature_ABfeature_BAfeature_BBfeature_CAfeature_CBid
Temperature1.000-0.0530.197-0.0470.2280.0010.171-0.626
feature_AA-0.0531.0000.3080.9750.2200.3840.103-0.096
feature_AB0.1970.3081.0000.3020.9230.2080.391-0.066
feature_BA-0.0470.9750.3021.0000.2390.2220.004-0.121
feature_BB0.2280.2200.9230.2391.0000.0710.120-0.099
feature_CA0.0010.3840.2080.2220.0711.0000.4710.145
feature_CB0.1710.1030.3910.0040.1200.4711.000-0.015
id-0.626-0.096-0.066-0.121-0.0990.145-0.0151.000

Missing values

2025-05-08T21:37:53.614806image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-08T21:37:53.730739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

iddatefeature_AAfeature_ABfeature_BAfeature_BBfeature_CAfeature_CBTemperature
19852198522017-01-23 19:00:0012.927-1.4748.884-2.6654.1120.4266.472000
16967169672016-12-24 17:45:009.913-0.3356.610-1.8833.2900.7616.824000
33101331012017-06-10 19:15:006.229-0.2683.447-0.1072.741-0.45714.773000
18433184332017-01-09 00:15:0011.5210.2689.808-0.3911.7060.5798.723000
50388503882017-12-07 21:00:008.7742.6125.6501.1373.0460.8225.065000
793179312016-09-21 14:45:005.7603.7513.8732.0972.1931.09723.988001
790879082016-09-21 09:00:007.5021.8084.9390.2132.1021.03623.636999
53045530452018-01-04 13:15:0011.1192.1437.5330.9593.7160.731-1.970000
47400474002017-11-06 18:00:008.4390.8044.584-0.8173.6861.34011.396000
28663286632017-04-25 13:45:000.8710.000-1.137-1.2082.3451.31016.954000
iddatefeature_AAfeature_ABfeature_BAfeature_BBfeature_CAfeature_CBTemperature
32179321792017-06-01 04:45:0011.2534.6228.1732.3812.4980.88321.737000
25546255462017-03-24 02:30:0014.9373.41610.4471.5994.1421.2495.276000
46610466102017-10-29 12:30:00-9.6453.349-11.7271.5991.9491.0369.708000
18735187352017-01-12 03:45:0012.5251.34010.6610.1421.7360.60910.482000
38560385602017-08-06 16:00:003.8182.210-1.1020.7114.9341.43223.426001
45499454992017-10-17 22:45:007.5021.5415.7920.3911.9190.9148.160000
53786537862018-01-12 06:30:0015.2713.41611.8332.1683.1370.6402.532000
60305603052018-03-21 04:15:0010.1814.1538.4573.1632.0710.6093.939000
218621862016-07-23 18:30:0010.5162.6126.9650.8533.5641.09740.237999
40633406332017-08-28 06:15:0011.9894.3547.4272.4164.3861.43216.108999